P2DCA: A Privacy-Preserving-Based Data Collection and Analysis Framework for IoMT Applications

Muhammad Usman, Mian Ahmad Jan, Xiangjian He, Jinjun Chen

Research output: Journal PublicationArticlepeer-review

34 Citations (Scopus)

Abstract

The concept of Internet of Multimedia Things (IoMT) is becoming popular nowadays and can be used in various smart city applications, e.g., traffic management, healthcare, and surveillance. In the IoMT, the devices, e.g., Multimedia Sensor Nodes (MSNs), are capable of generating both multimedia and non-multimedia data. The generated data are forwarded to a cloud server via a Base Station (BS). However, it is possible that the Internet connection between the BS and the cloud server may be temporarily down. The limited computational resources restrict the MSNs from holding the captured data for a longer time. In this situation, mobile sinks can be utilized to collect data from MSNs and upload to the cloud server. However, this data collection may create privacy issues, such as revealing identities and location information of MSNs. Therefore, there is a need to preserve the privacy of MSNs during mobile data collection. In this paper, we propose an efficient privacy-preserving-based data collection and analysis (P2DCA) framework for IoMT applications. The proposed framework partitions an underlying wireless multimedia sensor network into multiple clusters. Each cluster is represented by a Cluster Head (CH). The CHs are responsible to protect the privacy of member MSNs through data and location coordinates aggregation. Later, the aggregated multimedia data are analyzed on the cloud server using a counter-propagation artificial neural network to extract meaningful information through segmentation. Experimental results show that the proposed framework outperforms the existing privacy-preserving schemes, and can be used to collect multimedia data in various IoMT applications.

Original languageEnglish
Article number8680760
Pages (from-to)1222-1230
Number of pages9
JournalIEEE Journal on Selected Areas in Communications
Volume37
Issue number6
DOIs
Publication statusPublished - Jun 2019
Externally publishedYes

Keywords

  • IoMT
  • MSNs
  • aggregation
  • clusters
  • counter-propagation artificial neural network
  • privacy

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Electrical and Electronic Engineering

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